code: def unwrap_distributed(state_dict):
      r"""torch.distributed.DistributedDataParallel wraps the model with an additional "module.".
      This function unwraps this layer so that the weights can be loaded on models with a single GPU.
  
      Args:
          state_dict: Original state_dict.
  
      Return:
          unwrapped_state_dict: Unwrapped state_dict.
      """
  
      return {
          k.replace("module.", ""): v for k, v in state_dict.items()
      }
  
file_path: audio/examples/pipeline_tacotron2/inference.py
imports: import argparse
  import os
  import random
  import sys
  from functools import partial
  import numpy
  import torch
  import torchaudio
  from datasets import InverseSpectralNormalization
  from text.text_preprocessing import available_phonemizers, available_symbol_set, get_symbol_list, text_to_sequence
  from torchaudio.models import Tacotron2, tacotron2
  from utils import prepare_input_sequence
label_desc: Use image node_id 2 for calling a global function with example usage: unwrap_distributed(state_dict) and returns: unknown